When Holt-Winters is Better Than ML | Anais Dotis-Georgiou | InfluxData

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ML gets a lot of hype, but its statistical predecessors are still immensely powerful, especially in the time series space. Error, trend, seasonality forecast (ETS), autoregressive integrated moving average (ARIMA), and Holt-Winters are three classical methods that are not only incredibly popular but also excellent time series predictors. In fact, these classical methods outperform several other ML methods including long short-term memory (LTSM) and recurrent neural networks (RNNs) in one-step forecasting

Technologie

Anais Dotis-Georgiou, DevRel, InﬂuxData
When Holt-Winters is Better

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About Me
○ Anais Jackie Dotis on LinkedIn
○ @art.anaisdg
Developer Advocate, InﬂuxData

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M3 Competition
Statistical and Machine Learning forecasting
methods: Concerns and ways forward”
Spyros Makridakis, Evangelos Spiliotis, Vassilios Assimakopoulos

© InﬂuxData. All rights reserved.
M4 Competition

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Hybrid Methods Win
“Slawek Smyl, a data scientist at Uber Technologies, which mixes ES
formulas with a recurrent neural network (RNN) forecasting engine. Smyl
clariﬁes that his method does not constitute a simple ensemble of
exponential smoothing and neural networks .Instead, the models are truly
hybrid algorithms in which all parameters, like the initial ES seasonality and
smoothing coefficients, are ﬁtted concurrently with the RNN weights by the
same gradient descent method. The improvement of this method over that
of Comb was close to an impressive %”
-TheM Competition: , timeseriesand forecasting
Methods. International Journal of Forecasting.

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Naive Method
where

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Single Exponential Smoothing

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Component Form

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Linear Regression
Overview of Optimization for Single Exponential
Smoothing

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Optimization of RSS for Linear Regression

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Holt-Winters’s Multiplicative
SES (For Reference)

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RSS vs RMSE
Numerical Method
Nedler-Mead Method

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Numerical Method Ex: LU Decomposition(An
Aside)

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Nedler-Mead

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HOLT_WINTERS() and Inﬂux

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holtWinters() in Flux
from(bucket: "NOAA_water_database")
|> range(start: - y)
|> ﬁlter(fn: (r) => r._ﬁeld == "water_level")
|> aggregateWindow(every: m, fn: ﬁrst).
|> holtWinters(n: , seasonality: , interval: m)
Assuming no offset

●
● HW Blog:
https://www.inﬂuxdata.com/blog/when-you
-want-holt-winters-instead-of-machine-lear
ning/
● Autocorrelation:
https://www.inﬂuxdata.com/blog/autocorrel
ation-in-time-series-data/
● Pitfalls of LSTM for univariate TS:
https://towardsdatascience.com/how-not-to
-use-machine-learning-for-time-series-forec
asting-avoiding-the-pitfalls-19f9d7adf424

● anais@inﬂuxdata.com
● Community Page
○ community.inﬂuxdata.com
● Community Slack

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Moving Average

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Quartile Range

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Contextual Anomaly

© InﬂuxData. All rights reserved.© InﬂuxData. All rights reserved.
Collective Anomaly

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Steinkamp, Clifford [InfluxData] | Closing Thoughts Day 1 | InfluxDays 2022

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In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

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What are drone anti-jamming systems? The drone anti-jamming systems and anti-spoof technology protect against interference, jamming, and spoofing of the UAVs. To protect their security, countries are beginning to research drone anti-jamming systems, also known as drone strike weapons. The anti-jam and anti-spoof technology protects against interference, jamming and spoofing. A drone strike weapon is a drone attack weapon that can attack and destroy enemy drones. So what is so unique about this amazing system?

What Are The Drone Anti-jamming Systems Technology?

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GenCyber Cyber Security Day Presentation

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Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

Safe Software

Finology Group – Insurtech Innovation Award 2024

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Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

Exploring the Future Potential of AI-Enabled Smartphone Processors

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How to Troubleshoot Apps for the Modern Connected Worker

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Heather Hedden, Senior Consultant at Enterprise Knowledge, presented “The Role of Taxonomy and Ontology in Semantic Layers” at a webinar hosted by Progress Semaphore on April 16, 2024. Taxonomies at their core enable effective tagging and retrieval of content, and combined with ontologies they extend to the management and understanding of related data. There are even greater benefits of taxonomies and ontologies to enhance your enterprise information architecture when applying them to a semantic layer. A survey by DBP-Institute found that enterprises using a semantic layer see their business outcomes improve by four times, while reducing their data and analytics costs. Extending taxonomies to a semantic layer can be a game-changing solution, allowing you to connect information silos, alleviate knowledge gaps, and derive new insights. Hedden, who specializes in taxonomy design and implementation, presented how the value of taxonomies shouldn’t reside in silos but be integrated with ontologies into a semantic layer. Learn about: - The essence and purpose of taxonomies and ontologies in information and knowledge management; - Advantages of semantic layers leveraging organizational taxonomies; and - Components and approaches to creating a semantic layer, including the integration of taxonomies and ontologies

The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf

Enterprise Knowledge

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Data Cloud, More than a CDP by Matt Robison

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Tech Trends Report 2024 Future Today Institute.pdf

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As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape

TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments

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Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

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Scaling API-first – The story of a global engineering organization

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Handwritten Text Recognition for manuscripts and early printed texts

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🐬 The future of MySQL is Postgres 🐘

RTylerCroy

What is a good lead in your organisation? Which leads are priority? What happens to leads? When sales and marketing give different answers to these questions, or perhaps aren't sure of the answers at all, frustrations build and opportunities are left on the table. Join us for an illuminating session with Cian McLoughlin, HubSpot Principal Customer Success Manager, as we look at that crucial piece of the customer journey in which leads are transferred from marketing to sales.

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

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Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

What Are The Drone Anti-jamming Systems Technology?

GenCyber Cyber Security Day Presentation

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2024: Domino Containers - The Next Step. News from the Domino Container commu...

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

Finology Group – Insurtech Innovation Award 2024

Exploring the Future Potential of AI-Enabled Smartphone Processors

How to Troubleshoot Apps for the Modern Connected Worker

The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf

GenAI Risks & Security Meetup 01052024.pdf

Histor y of HAM Radio presentation slide

Data Cloud, More than a CDP by Matt Robison

Tech Trends Report 2024 Future Today Institute.pdf

TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

Scaling API-first – The story of a global engineering organization

Handwritten Text Recognition for manuscripts and early printed texts

🐬 The future of MySQL is Postgres 🐘

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When Holt-Winters is Better Than ML | Anais Dotis-Georgiou | InfluxData

1. Anais Dotis-Georgiou, DevRel, InﬂuxData When Holt-Winters is Better

5. © InfluxData. All rights reserved. Hybrid Methods Win “Slawek Smyl, a data scientist at Uber Technologies, which mixes ES formulas with a recurrent neural network (RNN) forecasting engine. Smyl clarifies that his method does not constitute a simple ensemble of exponential smoothing and neural networks .Instead, the models are truly hybrid algorithms in which all parameters, like the initial ES seasonality and smoothing coefficients, are fitted concurrently with the RNN weights by the same gradient descent method. The improvement of this method over that of Comb was close to an impressive %” -TheM Competition: , timeseriesand forecasting Methods. International Journal of Forecasting.

26. © InfluxData. All rights reserved. holtWinters() in Flux from(bucket: "NOAA_water_database") |> range(start: - y) |> filter(fn: (r) => r._field == "water_level") |> aggregateWindow(every: m, fn: first). |> holtWinters(n: , seasonality: , interval: m) Assuming no offset

27. ● ● HW Blog: https://www.inﬂuxdata.com/blog/when-you -want-holt-winters-instead-of-machine-lear ning/ ● Autocorrelation: https://www.inﬂuxdata.com/blog/autocorrel ation-in-time-series-data/ ● Pitfalls of LSTM for univariate TS: https://towardsdatascience.com/how-not-to -use-machine-learning-for-time-series-forec asting-avoiding-the-pitfalls-19f9d7adf424

28. ● anais@inﬂuxdata.com ● Community Page ○ community.inﬂuxdata.com ● Community Slack

29. Thank You!!

When Holt-Winters is Better Than ML | Anais Dotis-Georgiou | InfluxData

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